---
title: "When do I need a data observability platform?"
sidebarTitle: "When to add data observability"
---


### If the consequences of data issues are high
If you are running performance marketing budgets of $millions, a data issue can result in a loss of hundreds of thousands of dollars. 
In these cases, the ability to detect and resolve issues fast is business-critical. It typically involves multiple teams and the ability to measure, track, and report on data quality.  

### If data is scaling faster than the data team 
The scale and complexity of modern data environments make it impossible for teams to manually manage quality without expanding the team.  A data observability platform enables automation and collaboration, ensuring data quality is maintained as data continues to grow, without impacting team efficiency. 

### Common use cases
If your data is being used in one of the following use cases, you should consider adding a data observability platform:
- Self-service analytics 
- Data activation
- Powering AI & ML products
- Embedded analytics 
- Performance marketing 
- Regulatory reporting 
- A/B testing and experiments 

## Why isn't the open-source package enough?
The open-source package was designed for engineers that want to monitor their dbt project. The Cloud Platform was designed to support the complex, multifaceted requirements of larger teams and organizations, providing a holistic observability solution.